Relaxed Quantization for Discretized Neural Networks

ICLR 2019 Christos LouizosMatthias ReisserTijmen BlankevoortEfstratios GavvesMax Welling

Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure... (read more)

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